Exploring consumers’ willingness to eat insects in Italy

2019 ◽  
Vol 121 (11) ◽  
pp. 2937-2950 ◽  
Author(s):  
Nadia Palmieri ◽  
Maria Angela Perito ◽  
Maria Carmela Macrì ◽  
Claudio Lupi
Keyword(s):  
Empirical Investigation ◽  
Explanatory Variable ◽  
Classification Performance ◽  
Sustainable Food ◽  
Content Type ◽  
Sample Classification ◽  
Italian Regions ◽  
Out Of Sample ◽  
Selection Operator ◽  
Special Case

Purpose The purpose of this paper is to investigate the main factors that may affect Italian consumers’ willingness to eat insects. Italy is a fairly special case among Western countries: in many Italian regions, there is old traditional food with insects. Design/methodology/approach Data come from a sample of 456 consumers living in four Italian regions. The empirical investigation involves several steps: modification of class distributions to obtain a balanced sample; model estimation using the least absolute shrinkage and selection operator; model evaluation using out-of-sample classification performance measures; and estimation of the “effect” of each explanatory variable via average predictive comparisons. The uncertainty associated with the whole procedure is evaluated using the bootstrap. Findings The interviewed consumers are generally unwilling to eat insect-based food. However, factors such as previous experience, taste expectations and attitude towards both new food experiences and sustainable food play an important role in shaping individual inclination towards eating insects. Research limitations/implications The sample analysed in this study is not representative of the whole national population, as it happens in most papers dealing with entomophagy. Originality/value The paper revisits the issue using a relatively large sample and sophisticated statistical methods. The likely average effect of each explanatory variable is estimated and discussed in detail. The results provide interesting insights on how to approach a hypothetical Italian consumer in view of the possible development of a new market for edible insects.

scholarly journals Property market modelling and forecasting: simple vs complex models

2015 ◽  
Vol 33 (4) ◽  
pp. 337-361 ◽  
Author(s):  
Arvydas Jadevicius ◽  
Simon Huston
Keyword(s):  
Explanatory Variable ◽  
Moving Average ◽  
Model Complexity ◽  
Property Market ◽  
Content Type ◽  
Commercial Property ◽  
Explanatory Variables ◽  
Bank Rate ◽  
Out Of Sample ◽  
Modelling And Forecasting

Purpose – The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market. Design/methodology/approach – The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research. Findings – The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models. Practical implications – The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures. Originality/value – The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting.

Modelling industry interdependency dynamics in a network context

2019 ◽  
Vol 37 (1) ◽  
pp. 50-70
Author(s):  
Ya Qian ◽  
Wolfgang Härdle ◽  
Cathy Yi-Hsuan Chen
Keyword(s):  
Network Analysis ◽  
Dynamic Network ◽  
Influential Factor ◽  
Content Type ◽  
Ratio Criterion ◽  
Economic Interpretation ◽  
Out Of Sample ◽  
Tail Events ◽  
Selection Operator ◽  
Direction Information

Purpose Interdependency among industries is vital for understanding economic structures and managing industrial portfolios. However, it is hard to precisely model the interconnecting structure among industries. One of the reasons is that the interdependencies show a different pattern in tail events. This paper aims to investigate industry interdependency with the tail events. Design/methodology/approach General predictive model of Rapach et al. (2016) is extended to an interdependency model via least absolute shrinkage and selection operator quantile regression and network analysis. A dynamic network approach was applied on the Fama–French industry portfolios to study the time-varying interdependencies. Findings A denser network with heterogeneous central industries is found in tail cases. Significant interdependency varieties across time are shown under dynamic network analysis. Market volatility is identified as an influential factor of industry connectedness as well as clustering tendency under both normal and tail cases. Moreover, combining dynamic network with prediction direction information into out-of-sample industry return forecasting, a lower tail case is obtained, which gives the most accurate prediction of one-month forward returns. Finally, the Sharpe ratio criterion prefers high-centrality portfolios when tail risks are considered. Originality/value This study examines the industry portfolio interactions under the framework of network analysis and also takes into consideration tail risks. The combination of economic interpretation and statistical methodology helps in having a clear investigation of industry interdependency. Moreover, a new trading strategy based on network centrality seems profitable in our data sample.

The role of innovation in building competitive advantages: an empirical investigation

2018 ◽  
Vol 21 (1) ◽  
pp. 44-69 ◽  
Author(s):  
Prodromos Chatzoglou ◽  
Dimitrios Chatzoudes
Keyword(s):  
Competitive Advantage ◽  
Conceptual Framework ◽  
Empirical Investigation ◽  
Primary Data ◽  
Empirical Literature ◽  
Competitive Advantages ◽  
Manufacturing Companies ◽  
Content Type ◽  
The Impact ◽  
Structured Questionnaire

Purpose Nowadays, innovation appears as one of the main driving forces of organisational success. Despite the above fact, its impact on the propensity of an organisation to develop and sustain a competitive advantage has not yet received sufficient empirical investigation. The purpose of this paper is to enhance the existing empirical literature by focusing on the antecedents of innovation and its impact on competitive advantage. It proposes a newly developed conceptual framework that adopts a three-step approach, highlighting areas that have rarely been simultaneously examined before. Design/methodology/approach The examination of the proposed conceptual framework was performed with the use of a newly developed structured questionnaire that was distributed to a group of Greek manufacturing companies. The questionnaire has been successfully completed by chief executive officers (CEOs) from 189 different companies. CEOs were used as key respondents due to their knowledge and experience. The reliability and the validity of the questionnaire were thoroughly examined. Empirical data were analysed using the structural equation modelling technique. The study is empirical (based on primary data), explanatory (examines cause and effect relationships), deductive (tests research hypotheses) and quantitative (includes the analysis of quantitative data collected with the use of a structured questionnaire). Findings Results indicate that knowledge management, intellectual capital, organisational capabilities and organisational culture have significant direct and indirect effects on innovation, underlining the importance of their simultaneous enhancement. Finally, the positive effect of innovation on the creation of competitive advantages is empirically validated, bridging the gap in the relevant literature and offering avenues for additional future research. Originality/value The causal relationship between innovation and competitive advantage, despite its significant theoretical support, has not been empirically validated. The present paper aspires to bridge this gap, investigating the impact of innovation on the development of competitive advantages. Moreover, the present study adopts a multidimensional approach that has never been explored in the existing innovation literature, making the examination of the proposed conceptual framework an interesting research topic.

scholarly journals Spatial Dynamics Regarding Geographical Concentration of Economic Activities. Italian NUTS-3 Analysis

2021 ◽  
Author(s):  
Stefano De Falco
Keyword(s):  
Empirical Investigation ◽  
Methodological Approach ◽  
Geographical Area ◽  
Spatial Dynamics ◽  
Agglomeration Economies ◽  
Economic Activities ◽  
Production Sector ◽  
Scientific Debate ◽  
Geographical Concentration ◽  
Italian Regions

AbstractFor several years, the themes concerning agglomeration economies have been approached from different perspectives in the scientific debate, as capable of triggering various positive features. The present research starts precisely where many others arrive, that is, given the value of these externalities, analyzing the spatial distribution of the geographical concentration of economic activities and the related influencing factors. To this end, in this contribution an explanatory investigation is carried out into the spatial dynamics deriving from main productive sectors’ concentration in some Italian regions. The proposed methodological approach is based respectively on the LISA spatial autocorrelation models and on the analysis of non-neighboring clusters to understand if the geographical area of reference and / or the particular production sector are influencing variables. The empirical investigation confirms the presence of a parametric interaction between factors related in some cases on the geographical context and in others on the productive sector.

scholarly journals Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Wenning Wang ◽  
Xuebin Liu ◽  
Xuanqin Mou
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Classification Problem ◽  
Classification Performance ◽  
Spectral Structure ◽  
Limited Sample ◽  
Sample Classification ◽  
Training Samples ◽  
Traditional Classification ◽  
Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

On portfolio optimization

2016 ◽  
Vol 17 (3) ◽  
pp. 295-309 ◽  
Author(s):  
Theo Berger ◽  
Christian Fieberg
Keyword(s):  
Minimum Variance ◽  
Factor Models ◽  
Scale Factor ◽  
Covariance Matrices ◽  
Portfolio Performance ◽  
Content Type ◽  
Portfolio Decisions ◽  
Multi Scale ◽  
Out Of Sample ◽  
Factor Returns

Purpose The purpose of this paper is to show how investors can incorporate the multi-scale nature of asset and factor returns into their portfolio decisions and to evaluate the out-of-sample performance of such strategies. Design/methodology/approach The authors decompose daily return series of common risk factors and of all stocks listed in the Dow Jones Industrial Index (DJI) from 2000 to 2015 into different time scales to separate short-term noise from long-run trends. Then, the authors apply various (multi-scale) factor models to determine variance-covariance matrices which are used for minimum variance portfolio selection. Finally, the portfolios are evaluated by their out-of-sample performance. Findings The authors find that portfolios which are constructed on variance-covariance matrices stemming from multi-scale factor models outperform portfolio allocations which do not take the multi-scale nature of asset and factor returns into account. Practical implications The results of this paper provide evidence that accounting for the multi-scale nature of return distributions in portfolio decisions might be a promising approach from a portfolio performance perspective. Originality/value The authors demonstrate how investors can incorporate the multi-scale nature of returns into their portfolio decisions by applying wavelet filter techniques.

A comparison of minimum variance and maximum Sharpe ratio portfolios for mainstream investors

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anja Vinzelberg ◽  
Benjamin Rainer Auer
Keyword(s):  
Minimum Variance ◽  
Sharpe Ratio ◽  
Optimization Techniques ◽  
Estimation Methods ◽  
Content Type ◽  
Single Index ◽  
Index Model ◽  
Single Index Model ◽  
Out Of Sample ◽  
Asset Classes

PurposeMotivated by the recent theoretical rehabilitation of mean-variance analysis, the authors revisit the question of whether minimum variance (MinVar) or maximum Sharpe ratio (MaxSR) investment weights are preferable in practical portfolio formation.Design/methodology/approachThe authors answer this question with a focus on mainstream investors which can be modeled by a preference for simple portfolio optimization techniques, a tendency to cling to past asset characteristics and a strong interest in index products. Specifically, in a rolling-window approach, the study compares the out-of-sample performance of MinVar and MaxSR portfolios in two asset universes covering multiple asset classes (via investable indices and their subindices) and for two popular input estimation methods (full covariance and single-index model).FindingsThe authors find that, regardless of the setting, there is no statistically significant difference between MinVar and MaxSR portfolio performance. Thus, the choice of approach does not matter for mainstream investors. In addition, the analysis reveals that, contrary to previous research, using a single-index model does not necessarily improve out-of-sample Sharpe ratios.Originality/valueThe study is the first to provide an in-depth comparison of MinVar and MaxSR returns which considers (1) multiple asset classes, (2) a single-index model and (3) state-of-the-art bootstrap performance tests.

Exploring persuasive sales techniques to improve customer acceptance of sustainable but unfamiliar menu in restaurants

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chris He Cai ◽  
Anni Ding ◽  
Tiffany Shin Legendre
Keyword(s):  
Food Waste ◽  
Information Sources ◽  
Social Stigma ◽  
Food Sources ◽  
Food Aversion ◽  
Sustainable Food ◽  
Content Type ◽  
Customer Acceptance ◽  
Unfamiliar Food ◽  
Sales Strategies

Purpose Although restauranteurs hope to incorporate offal or variety meat, in the menu as an attempt to reduce food waste, adopting these ingredients is still challenging due to customer rejections. This study aims to propose potential persuasive sales strategies based on customers’ different information sources to increase organ meat-based menu sales for restaurateurs. Design/methodology/approach In this research, a qualitative study was conducted to identify critical factors that show persuasive effects from external, interpersonal and experiential information sources. A total of 20 in-depth expert interviews with professional chefs and restauranteurs were conducted and key persuasive service tactics were analyzed. Findings From their experience of persuading customers to try unusual foods, insights about how to alleviate unfamiliar food aversion were obtained. The findings of this study showed that different persuasive sales tactics can be implemented to decrease customers’ aversion to offal and offcuts on menus. Research limitations/implications The context of offal is meaningful theoretically because it sheds light on the literature gaps related to persuasive sales strategies for food products with a negative social stigma. Practically, the findings of this study explicitly address that offal usage in restaurants can not only encourage the culinary uniqueness of a restaurant but also contribute to the reduction of food waste by foodservice operations. Originality/value This research answers the calls for more research on sustainable food sources in hospitality literature by proposing offal as a potential alternative protein source. The findings of this study can further be used to improve customer acceptance of other sustainable but unfamiliar food items.

scholarly journals An analytical framework for employment regulation: investigating the regulatory space

2017 ◽  
Vol 39 (3) ◽  
pp. 291-307 ◽  
Author(s):  
Cristina Inversi ◽  
Lucy Ann Buckley ◽  
Tony Dundon
Keyword(s):  
Design Methodology ◽  
Empirical Investigation ◽  
Analytical Framework ◽  
Legal Aspects ◽  
Theoretical Development ◽  
Space Theory ◽  
Content Type ◽  
Labour Policy ◽  
Regulatory Approach ◽  
Multi Level

Purpose The purpose of this paper is to advance a conceptual analytical framework to help explain employment regulation as a dynamic process shaped by institutions and actors. The paper builds on and advances regulatory space theory. Design/methodology/approach The paper analyses the literature on regulatory theory and engages with its theoretical development. Findings The paper advances the case for a broader and more inclusive regulatory approach to better capture the complex reality of employment regulation. Further, the paper engages in debates about the complexity of employment regulation by adopting a multi-level perspective. Research limitations/implications The research proposes an analytical framework and invites future empirical investigation. Originality/value The paper contends that existing literature affords too much attention to a (false) regulation vs deregulation dichotomy, with insufficient analysis of other “spaces” in which labour policy and regulation are formed and re-formed. In particular, the proposed framework analyses four different regulatory dimensions, combining the legal aspects of regulation with self-regulatory dimensions of employment regulation.

“Speaking truth to power”: analysing shadow reporting as a form of shadow accounting

2017 ◽  
Vol 30 (3) ◽  
pp. 510-533 ◽  
Author(s):  
Helen Tregidga
Keyword(s):  
Power Relations ◽  
Design Methodology ◽  
Empirical Investigation ◽  
Single Case ◽  
Senior Management ◽  
Single Case Study ◽  
Content Type ◽  
Transformative Potential ◽  
Perceived Impacts

Purpose The purpose of this paper is to empirically investigate the act of shadow reporting by a social movement organisation as a form of shadow accounting within a sustained campaign against a target corporation. Situated within a consideration of power relations, the rationales underlying the production of the shadow report, and the shadow reports perceived value and limits as a shadow accounting mechanism, are investigated. Design/methodology/approach A Foucauldian approach to power/knowledge and truth is drawn upon in the analysis of a single case study. Alongside a consideration of the shadow report itself, interviews with both the preparers of the report and senior management of the corporation targeted comprise the main data. Findings The paper provides an empirical investigation into shadow reporting as a form of shadow accounting. While a range of insights are garnered into the preparation, dissemination and impact of the shadow report, key findings relate to a consideration of power relations. The perceived “truth” status of corporate accounts compared to accounts prepared by shadow accountants is problematised through a consideration of technologies of power and power/knowledge formations. Power relations are subsequently recognised as fundamental to the emancipatory potential of shadow reporting. Research limitations/implications Results from a single case study are presented. Furthermore, given the production of the shadow report occurred several years prior to the collection of data, participants were asked to reflect on past events. Findings are therefore based on those reflections. Originality/value While previous studies have considered the preparation of shadow reports and their transformative potential, this study is, the author believes, the first to empirically analyse the preparation, dissemination and perceived impacts of shadow reporting from the perspectives of both the shadow report producers and the target corporation.

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